Science Journal of Applied Mathematics and Statistics

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The Application of Binary Logistic Regression Analysis on Staff Performance Appraisal

Received: 05 June 2017    Accepted: 14 June 2017    Published: 26 July 2017
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Abstract

This study investigates and models salient factors that influences the performance of staff in an appraisal exercise and as well estimate the odds of these factors influencing the outcome variable (performance rating) as compared to their reference group or category. The Binary Logistic regression model was used to estimate chance of the staff given the influence of the identified independent variables. In the study, marital status was found to be significant in distinguishing staff performance as identified from the outlined factors influencing their performance.

DOI 10.11648/j.sjams.20170504.15
Published in Science Journal of Applied Mathematics and Statistics (Volume 5, Issue 4, August 2017)
Page(s) 164-168
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Performance Appraisal, Binary Logistic, Odds

References
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[8] BOSWELL, W. R. & Boudreau, J. W. (2002) Separating the development and evaluative performance appraisal uses. Journal of Business and Psychology. Vol 16, pp 391-412.
[9] Bird, P. (2003) Performance Appraisals. London, Hodder and Stoughton.
[10] Brumbach, G. (1998) Some ideas, issues and prediction about performance management. Public Personnel Management. Winter, pp 387-402.
[11] De Nisi, A. S. (1996) Cognitive approach to performance appraisal. London, Routledge.
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[21] McMASTER, M. (1994) Performance Management. Oregon, Metamorphous Press.
[22] Roberts, G. & Pregister, M. (2007) Why employes dislike performance appraisals. Regent Global Business Review. Vol 1, no. 1, pp 14-21.
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[24] S. Kumar and D. Toshniwal, “A data mining framework to analyze road accident data”, Journal of Big Data, Springer, vol. 2, No. 26, pp. 1-18, 2015.
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Author Information
  • Department of Mathematics, Arthur Jarvis University of Science and Technology, Calabar, Nigeria

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  • APA Style

    Runyi Emmanuel Francis. (2017). The Application of Binary Logistic Regression Analysis on Staff Performance Appraisal. Science Journal of Applied Mathematics and Statistics, 5(4), 164-168. https://doi.org/10.11648/j.sjams.20170504.15

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    ACS Style

    Runyi Emmanuel Francis. The Application of Binary Logistic Regression Analysis on Staff Performance Appraisal. Sci. J. Appl. Math. Stat. 2017, 5(4), 164-168. doi: 10.11648/j.sjams.20170504.15

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    AMA Style

    Runyi Emmanuel Francis. The Application of Binary Logistic Regression Analysis on Staff Performance Appraisal. Sci J Appl Math Stat. 2017;5(4):164-168. doi: 10.11648/j.sjams.20170504.15

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  • @article{10.11648/j.sjams.20170504.15,
      author = {Runyi Emmanuel Francis},
      title = {The Application of Binary Logistic Regression Analysis on Staff Performance Appraisal},
      journal = {Science Journal of Applied Mathematics and Statistics},
      volume = {5},
      number = {4},
      pages = {164-168},
      doi = {10.11648/j.sjams.20170504.15},
      url = {https://doi.org/10.11648/j.sjams.20170504.15},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.sjams.20170504.15},
      abstract = {This study investigates and models salient factors that influences the performance of staff in an appraisal exercise and as well estimate the odds of these factors influencing the outcome variable (performance rating) as compared to their reference group or category. The Binary Logistic regression model was used to estimate chance of the staff given the influence of the identified independent variables. In the study, marital status was found to be significant in distinguishing staff performance as identified from the outlined factors influencing their performance.},
     year = {2017}
    }
    

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